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Python function attributes – uses and abuses [closed]

Posted by: admin November 1, 2017 Leave a comment


Not many are aware of this feature, but Python’s functions (and methods) can have attributes. Behold:

>>> def foo(x):
...     pass
>>> foo.score = 10
>>> dir(foo)
['__call__', '__class__', '__delattr__', '__dict__', '__doc__', '__get__', '__getattribute__', '__hash__', '__init__', '__module__', '__name__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__str__', 'func_closure', 'func_code', 'func_defaults', 'func_dict', 'func_doc', 'func_globals', 'func_name', 'score']
>>> foo.score
>>> foo.score += 1
>>> foo.score

What are the possible uses and abuses of this feature in Python ? One good use I’m aware of is PLY‘s usage of the docstring to associate a syntax rule with a method. But what about custom attributes ? Are there good reasons to use them ?


I typically use function attributes as storage for annotations. Suppose I want to write, in the style of C# (indicating that a certain method should be part of the web service interface)

class Foo(WebService):
    def bar(self, arg1, arg2):

then I can define

def webmethod(func):
    func.is_webmethod = True
    return func

Then, when a webservice call arrives, I look up the method, check whether the underlying function has the is_webmethod attribute (the actual value is irrelevant), and refuse the service if the method is absent or not meant to be called over the web.


I’ve used them as static variables for a function. For example, given the following C code:

int fn(int i)
    static f = 1;
    f += i;
    return f;

I can implement the function similarly in Python:

def fn(i):
    fn.f += i
    return fn.f
fn.f = 1

This would definitely fall into the “abuses” end of the spectrum.


You can do objects the JavaScript way… It makes no sense but it works 😉

>>> def FakeObject():
...   def test():
...     print "foo"
...   FakeObject.test = test
...   return FakeObject
>>> x = FakeObject()
>>> x.test()


Function attributes can be used to write light-weight closures that wrap code and associated data together:

#!/usr/bin/env python

SW_MARK  = 1
SW_BASE  = 2

def stopwatch():
   import time

   def _sw( action = SW_DELTA ):

      if action == SW_DELTA:
         return time.time() - _sw._time

      elif action == SW_MARK:
         _sw._time = time.time()
         return _sw._time

      elif action == SW_BASE:
         return _sw._time

         raise NotImplementedError

   _sw._time = time.time() # time of creation

   return _sw

# test code
import os
os.system("sleep 1")
print sw() # defaults to "SW_DELTA"
sw( SW_MARK )
os.system("sleep 2")
print sw()
print sw2()





I use them sparingly, but they can be pretty convenient:

def log(msg):

Now I can use log throughout my module, and redirect output simply by setting log.logfile. There are lots and lots of other ways to accomplish that, but this one’s lightweight and dirt simple. And while it smelled funny the first time I did it, I’ve come to believe that it smells better than having a global logfile variable.


Sometimes I use an attribute of a function for caching already computed values. You can also have a generic decorator that generalizes this approach. Be aware of concurrency issues and side effects of such functions!


I was always of the assumption that the only reason this was possible was so there was a logical place to put a doc-string or other such stuff. I know if I used it for any production code it’d confuse most who read it.


I’ve created this helper decorator to easily set function attributes:

def with_attrs(**func_attrs):
    """Set attributes in the decorated function, at definition time.
    Only accepts keyword arguments.
        @with_attrs(counter=0, something='boing')
        def count_it():
            count_it.counter += 1
        print count_it.counter
        print count_it.something
        # Out:
        # >>> 0
        # >>> 'boing'
    def attr_decorator(fn):
        def wrapper(*args, **kwargs):
            return fn(*args, **kwargs)

        for attr, value in func_attrs.iteritems():
            setattr(wrapper, attr, value)

        return wrapper

    return attr_decorator

A use case is to create a collection of factories and query the data type they can create at a function meta level.
For example (very dumb one):

def factory1():
    return [1, 2, 3]

def factory2():
    return SomeClass()

factories = [factory1, factory2]

def create(datatype):
    for f in factories:
        if f.datatype == datatype:
            return f()
    return None